R Notebook

data<-trade_zone_table

Countries<-c("Algeria",
"Angola",
"Benin",
"Botswana",
"Burkina Faso",
'Burundi',
"Cabo Verde",
"Cameroon",
'Central African Republic',
"Chad",
"Comoros",
"Cote d'Ivoire",
'Democratic Republic of the Congo',
"Djibouti",
"Egypt",
'Equatorial Guinea',
"Eritrea",
"Ethiopia",
"Gabon",
"Gambia",
"Ghana",
"Guinea",
"Guinea-Bissau",
"Kenya",
"Lesotho",
"Liberia",
"Libya",
"Madagascar",
"Malawi",
"Mali",
"Mauritania",
"Mauritius",
"Morocco",
"Mozambique",
"Namibia",
"Niger",
"Nigeria",
'Republic of the Congo',
"Rwanda",
'Sao Tome and Principe',
'Senegal',
'Seychelles',
'Sierra Leone',
'Somalia',
'South Africa',
'South Sudan',
'Sudan',
'Swaziland',
'Tanzania',
'Togo',
'Tunisia',
'Uganda',
'Zambia',
'Zimbabwe'
)

library(dplyr)
data[is.na(data)]<-0
data

# Country_1 <-c(1,1,0,0)
# Country_2 <-c(1,0,1,0)
# Country_3 <-c(1,0,1,0)
# Country_4 <-c(0,1,0,1)
# Country_5 <-c(0,0,1,1)
# 
# Countries<-c("Country_1","Country_2","Country_3","Country_4","Country_5")
# Zone_1<-c(1,1,1,0,0)
# Zone_2<-c(1,0,0,1,0)
# Zone_3<-c(0,1,1,0,1)
# Zone_4<-c(0,0,0,1,1)
library(tidygraph)
data<-as.matrix(data)
rownames(data) <- paste(Countries)
as_tbl_graph(graph.incidence(data))

# A tbl_graph: 70 nodes and 153 edges
#
# A bipartite simple graph with 1 component
#
# Node Data: 70 x 2 (active)
  type  name        
  <lgl> <chr>       
1 FALSE Algeria     
2 FALSE Angola      
3 FALSE Benin       
4 FALSE Botswana    
5 FALSE Burkina Faso
6 FALSE Burundi     
# ... with 64 more rows
#
# Edge Data: 153 x 2
   from    to
  <int> <int>
1     1    67
2     2    55
3     2    57
# ... with 150 more rows

library(ggraph)
as_tbl_graph(graph.incidence(data)) %>% 
  ggraph()+
  geom_node_point(aes(shape = type, size = 20), show.legend = F)+
  geom_node_text(aes(label = name), colour = 'black', vjust = 4)+
  geom_edge_link()+
  theme_graph()

Using `nicely` as default layout

data_project<- bipartite.projection(graph.incidence(data)) 
data_project

$proj1
IGRAPH f28c147 UNW- 54 728 -- 
+ attr: name (v/c), weight (e/n)
+ edges from f28c147 (vertex names):
 [1] Algeria--Libya                    Algeria--Mauritania              
 [3] Algeria--Morocco                  Algeria--Tunisia                 
 [5] Angola --Botswana                 Angola --Comoros                 
 [7] Angola --Cote d'Ivoire            Angola --Lesotho                 
 [9] Angola --Madagascar               Angola --Malawi                  
[11] Angola --Mauritius                Angola --Mozambique              
[13] Angola --Namibia                  Angola --Seychelles              
[15] Angola --South Africa             Angola --Tanzania                
+ ... omitted several edges

$proj2
IGRAPH 9f28a5a UNW- 16 42 -- 
+ attr: name (v/c), weight (e/n)
+ edges from 9f28a5a (vertex names):
 [1] SADC   --ECCAS   SADC   --SACU    SADC   --CEPGL   SADC   --COMESA  SADC   --CEN-SAD
 [6] SADC   --EAC     SACU   --WAMZ    SACU   --ECOWAS  SACU   --CEN-SAD SACU   --COMESA 
[11] ECCAS  --CEPGL   ECCAS  --EAC     ECCAS  --COMESA  ECCAS  --CEMAC   ECCAS  --CEN-SAD
[16] CEPGL  --EAC     CEPGL  --COMESA  EAC    --COMESA  EAC    --CEN-SAD EAC    --IGAD   
[21] EAC    --IGAD__1 CEMAC  --CEN-SAD WAMZ   --ECOWAS  WAMZ   --CEN-SAD WAMZ   --MRU    
[26] MRU    --ECOWAS  MRU    --UEMCA   MRU    --CEN-SAD ECOWAS --UEMCA   ECOWAS --CEN-SAD
[31] ECOWAS --LGA     LGA    --UEMCA   LGA    --CEN-SAD UEMCA  --CEN-SAD CEN-SAD--COMESA 
[36] CEN-SAD--IGAD    CEN-SAD--IGAD__1 CEN-SAD--UMA     UMA    --COMESA  COMESA --IGAD   
+ ... omitted several edges

as_tbl_graph(data_project$proj1)

# A tbl_graph: 54 nodes and 728 edges
#
# An undirected simple graph with 1 component
#
# Node Data: 54 x 1 (active)
  name        
  <chr>       
1 Algeria     
2 Angola      
3 Benin       
4 Botswana    
5 Burkina Faso
6 Burundi     
# ... with 48 more rows
#
# Edge Data: 728 x 3
   from    to weight
  <int> <int>  <dbl>
1     1    27      1
2     1    31      1
3     1    33      1
# ... with 725 more rows

country_data<-as_tbl_graph(data_project$proj1)

as_tbl_graph(data_project$proj1) %>% 
  ggraph()+
  geom_node_point(aes(size = 20), show.legend = F)+
  geom_node_text(aes(label = name), colour = 'black', vjust = 4)+
  geom_edge_link()+
  theme_graph()

Using `nicely` as default layout

# random_graph <- sample_gnp(n = 100, 0.2)
graph_data<-as_tbl_graph(data)
# 
# GLI_rg <- as_tbl_graph(country_data) %>%
#   mutate(size = graph_order(),
#          edges = graph_size(),
#          density = edge_density(country_data),
#          average_distance = graph_mean_dist(),
#          trans = transitivity(country_data, type = "global")
#   ) %>%
#   as.data.frame()
# 
# GLI_rg
info<-country_data%>%
  activate(nodes)%>%
  mutate(betweenness = centrality_betweenness(normalized = TRUE),
         closeness = centrality_closeness(normalized = TRUE),
         transitivity = transitivity(country_data,type = "local"),
         degree = degree(country_data))%>%
  select(name,betweenness,closeness,transitivity,degree)%>%
  as.data.frame()
info

library(ggplot2)
data.frame(info)%>%
  ggplot(aes(x=betweenness,y=closeness, label=name))+
  geom_point()+geom_text()+scale_x_log10()+geom_smooth(method="lm",size = 0.5)+ylab("Closeness Centrality")+xlab("betweenness")

#define variables to all to allinfo variable, ie gdp_data and mortality stats
allinfo$GDP.growth.rate<-as.numeric(allinfo$GDP.growth.rate)

data.frame(allinfo)%>%
  ggplot(aes(x=GDP.perCapita,y=closeness, label=name))+
  geom_point()+geom_text()+geom_smooth(method="lm",size = 0.5)+scale_x_log10()+scale_y_log10()

data.frame(allinfo)%>%
  ggplot(aes(x=GDP.perCapita,y=betweenness, label=name))+
  geom_point()+geom_text()+geom_smooth(method="lm",size = 0.5)+scale_x_log10()+scale_y_log10()

data.frame(allinfo)%>%
  ggplot(aes(y=GDP.perCapita,x=degree, label=name))+
  geom_point()+geom_text()+geom_smooth(method="lm",size = 0.5)+scale_x_log10()+scale_y_log10()

data.frame(allinfo)%>%
  ggplot(aes(x=African.rank,y=closeness, label=name))+
  geom_point()+geom_text()+geom_smooth(method="lm",size = 0.5)+scale_x_log10()+scale_y_log10()+xlab("African.rank")+ylab("closeness")

data.frame(allinfo)%>%
  ggplot(aes(x=African.rank,y=betweenness, label=name))+
  geom_point()+geom_text()+geom_smooth(method="lm",size = 0.5)+scale_x_log10()+scale_y_log10()+xlab("African.rank")+ylab("betweenness")

data.frame(allinfo)%>%
  ggplot(aes(x=African.rank,y=degree, label=name))+
  geom_point()+geom_text()+geom_smooth(method="lm",size = 0.5)+scale_x_log10()+scale_y_log10()

data.frame(allinfo)%>%
  ggplot(aes(x=degree,y=African.rank, label=name))+
  geom_point()+geom_text()+geom_smooth(method="lm",size = 0.5)+scale_x_log10()+scale_y_log10()

data.frame(allinfo)%>%
  ggplot(aes(x=World.rank,y=closeness, label=name))+
  geom_point()+geom_text()+geom_smooth(method="lm",size = 0.5)+scale_x_log10()+scale_y_log10()+xlab("World.rank")+ylab("closeness")

data.frame(allinfo)%>%
  ggplot(aes(x=World.rank,y=betweenness, label=name))+
  geom_point()+geom_text()+geom_smooth(method="lm",size = 0.5)+scale_x_log10()+scale_y_log10()+xlab("World.rank")+ylab("betweenness")

data.frame(allinfo)%>%
  ggplot(aes(x=World.rank,y=degree, label=name))+
  geom_point()+geom_text()+geom_smooth(method="lm",size = 0.5)+scale_x_log10()+scale_y_log10()+xlab("World.rank")+ylab("degree")

data.frame(allinfo)%>%
  ggplot(aes(x=degree,y=World.rank, label=name))+
  geom_point()+geom_text()+geom_smooth(method="lm",size = 0.5)+scale_x_log10()+scale_y_log10()+ylab("World.rank")+xlab("degree")

data.frame(allinfo)%>%
  ggplot(aes(x=GDP.from.agriculture,y=closeness, label=name))+
  geom_point()+geom_text()+geom_smooth(method="lm",size = 0.5)+scale_x_log10()+scale_y_log10()+xlab("GDP.from.agriculture")+ylab("closeness")

Error in Math.factor(x, base) : 'log' not meaningful for factors

rr data.frame(allinfo)%>% ggplot(aes(x=GDP..industry,y=closeness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()+xlab(..industry)+ylab()

rr data.frame(allinfo)%>% ggplot(aes(x=GDP..industry,y=betweenness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()+xlab(..industry)+ylab()

rr data.frame(allinfo)%>% ggplot(aes(x=GDP..industry,y=degree, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()+xlab(..industry)+ylab()

rr data.frame(allinfo)%>% ggplot(aes(x=closeness,y=GDP..industry, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=betweenness,y=GDP..industry, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=degree,y=GDP..industry, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=GDP.services,y=closeness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()+xlab(.services)+ylab()

rr data.frame(allinfo)%>% ggplot(aes(x=GDP.services,y=betweenness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()+xlab(.services)+ylab()

rr data.frame(allinfo)%>% ggplot(aes(x=GDP.services,y=degree, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()+xlab(.services)+ylab()

rr data.frame(allinfo)%>% ggplot(aes(x=GDP.estimates,y=closeness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()+xlab(.estimates)+ylab()

rr data.frame(allinfo)%>% ggplot(aes(x=GDP.estimates,y=betweenness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()+xlab(.estimates)+ylab()

rr data.frame(allinfo)%>% ggplot(aes(x=GDP.estimates,y=degree, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()+xlab(.estimates)+ylab()

rr data.frame(allinfo)%>% ggplot(aes(x=GDP.growth.rate,y=closeness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()+xlab(.growth.rate)+ylab()

rr data.frame(allinfo)%>% ggplot(aes(x=GDP.growth.rate,y=betweenness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()+xlab(.growth.rate)+ylab()

rr data.frame(allinfo)%>% ggplot(aes(x=GDP.growth.rate,y=degree, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()+xlab(.growth.rate)+ylab()

rr ggplot(data.frame(allinfo),aes(x=name,y=closeness))+geom_bar(stat=)+theme(axis.text.x = element_text(angle = 90, hjust = 1))

rr ggplot(data.frame(allinfo),aes(x=name,y=betweenness))+geom_bar(stat=)+theme(axis.text.x = element_text(angle = 90, hjust = 1))

rr ggplot(data.frame(allinfo),aes(x=name,y=degree))+geom_bar(stat=)+theme(axis.text.x = element_text(angle = 90, hjust = 1))

rr data.frame(allinfo)%>% ggplot(aes(x=closeness,y=LifeExp, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=betweenness,y=LifeExp, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=degree,y=LifeExp, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(y=Neonatel,x=closeness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(y=Neonatel,x=betweenness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(y=Neonatel,x=degree, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=Infant_Mortality,y=closeness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=Infant_Mortality,y=betweenness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=Infant_Mortality,y=degree, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(y=Infant_Mortality,x=closeness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(y=Infant_Mortality,x=betweenness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(y=Infant_Mortality,x=degree, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=under_5,y=closeness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=under_5,y=betweenness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=under_5,y=degree, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(y=under_5,x=closeness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(y=under_5,x=betweenness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(y=under_5,x=degree, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=Male_adult_mortality,y=closeness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=Male_adult_mortality,y=betweenness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=Male_adult_mortality,y=degree, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(y=Male_adult_mortality,x=closeness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(y=Male_adult_mortality,x=betweenness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(y=Male_adult_mortality,x=degree, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=female_adult_mortality,y=closeness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=female_adult_mortality,y=betweenness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(x=female_adult_mortality,y=degree, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(y=female_adult_mortality,x=closeness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(y=female_adult_mortality,x=betweenness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr data.frame(allinfo)%>% ggplot(aes(y=female_adult_mortality,x=degree, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

rr

data.frame(allinfo)%>% ggplot(aes(x=Infant_Mortality,y=closeness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

data.frame(allinfo)%>% ggplot(aes(x=Infant_Mortality,y=betweenness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

data.frame(allinfo)%>% ggplot(aes(x=Infant_Mortality,y=degree, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

data.frame(allinfo)%>% ggplot(aes(y=Infant_Mortality,x=closeness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

data.frame(allinfo)%>% ggplot(aes(y=Infant_Mortality,x=betweenness, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

data.frame(allinfo)%>% ggplot(aes(y=Infant_Mortality,x=degree, label=name))+ geom_point()+geom_text()+geom_smooth(method=,size = 0.5)+scale_x_log10()+scale_y_log10()

data.frame(allinfo)%>%
  ggplot(aes(y=closeness,x=degree, label=name))+
  geom_point()+geom_text()+geom_smooth(method="lm",size = 0.5)+scale_x_log10()+scale_y_log10()

allinfo<-data.frame(c(info, gdp_data, mortal))
trade_info <-data.frame(allinfo, african_trade_items)
data.frame(trade_info) %>% 
  ggplot(aes(x=resource, y=GDP.perCapita, label=name.1, color=resource)) +
  geom_point() +
  geom_text() +
  geom_smooth(method="lm", size=0.5) +
  ylab("GDP") + xlab("Country") +
  labs(title="Ordered Bar Chart", 
  subtitle="Make Vs Avg. Mileage", 
  caption="source: mpg") + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6))

temp <- trade_info %>%
  group_by(name.1) %>%
  mutate(GDP_industry = GDP.perCapita*(GDP..industry/100),
         GDP_agri = GDP.perCapita*(GDP.from.agriculture/100),
         GDP_services = GDP.perCapita*(GDP.services/100)) %>%
  select(GDP.perCapita,GDP_industry,GDP_agri,GDP_services)

Adding missing grouping variables: `name.1`

data.frame(trade_info) %>% 
  ggplot(aes(x=name.1, y=GDP.perCapita, label=name.1, fill=resource)) +
  facet_wrap(~resource) +
  geom_col() +
  geom_text() +
  geom_smooth(method="lm", size=0.5) +
  ylab("GDP") + xlab("Country") +
  labs(title="Ordered Bar Chart", 
  subtitle="Make Vs Avg. Mileage", 
  caption="source: mpg") + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6))